Data Visualization Techniques
Venustiano Soancatl Aguilar
Scientific Visualization
“The use of computers or techniques for comprehending data or to extract knowledge from the results of simulations, computations, or measurements”.
Examples: CT Scans, MRI, Inertial sensors.
[McCormick et al., 1987]
Information Visualization
“Visualization applied to abstract quantities and relations in order to get insight in the data”.
Examples: matplotlib, seaborn, ggplot2
[Chi, 2000]
Visual Analytics
“The science of analytical reasoning facilitated by interactive visual interfaces”.
Examples: Tableau, Plotly, Bokeh, Panel, etc.
[Thomas and Cook, 2005]
When is visualization useful?
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Too much data:
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do not have time to analyze it all (or read the analysis results)
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show an overview, discover which questions are relevant
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refine search either visually or analytically
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Qualitative / complex questions:
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cannot capture the question compactly/exactly in a query
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question/goal is inherently qualitative: understand what is going on
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show an overview, answer the question by seeing relevant patterns
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Communication:
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transfer results to different (non-technical) stakeholders
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learn about a new domain or problem
Why is Visualization Useful?
- (in)validate the fit of a given model with a dataset
- find the distribution of values over a given domain
- find the correlation (or lack thereof) of several variables
- answer precise (quantitative) questions
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Anscombe’s quartet — original datasets described by F. J. Anscombe (1973). Image file: Wikimedia Commons: Anscombe.svg. See the Wikipedia article “Anscombe’s quartet” for background and references. Accessed 2025-10-26.
Why is Visualization?
- find support for a new model in the data
- find which model best fits a dataset
- find the phenomenon behind the data
- answer more vague (qualitative) questions